An overview of ultrasound-derived radiomics and deep learning in liver
Di Zhang, Xian‐Ya Zhang, Yayang Duan, Christoph F. Dietrich, Xin‐Wu Cui, Chaoxue Zhang
Abstract
Over the past few years, developments in artificial intelligence (AI), especially in radiomics and deep learning, have enabled the extraction of pathophysiology-related information from varied medical imaging and are progressively transforming medical practice. AI applications are extending into domains previously thought to be accessible only to human experts. Recent research has demonstrated that ultrasound-derived radiomics and deep learning represent an enticing opportunity to benefit preoperative evaluation and prognostic monitoring of diffuse and focal liver disease. This review summarizes the application of radiomics and deep learning in ultrasound liver imaging, including identifying focal liver lesions and staging of liver fibrosis, as well as the evaluation of pathobiological properties of malignant tumors and the assessment of recurrence and prognosis. Besides, we identify important hurdles that must be overcome while also discussing the challenges and opportunities of radiomics and deep learning in clinical applications.